A communication system should ensure fairness and quality of service to its users through efficient resource utilization.
Mathematically, the power allocation problem can be formulated as the problem of optimizing a certain system-level utility function – such as sum-rate, min-rate, and harmonic-rate – subject to resource budget constraints.
Despite the remarkable success of this paradigm [1]}, it turns out that many of the formulated optimization problems are non-convex and NP-hard, making them difficult to solve [2]}, [3]}, [4]}.
Hence, a plethora of classical methods have been developed to obtain approximate solutions to these challenging optimization problems.
For the canonical case of sum-rate maximization, the algorithms developed over the past decades can be broadly categorized into those based on Lagrangian dual decomposition [5]}, [6]}, interference pricing [7]}, [8]}, successive convex approximation [9]}, and weighted minimum mean squared error (WMMSE) minimization [10]}.
Of special interest to us due to its widespread adoption is this last category of classical methods.
Common shortcomings of this body of work include high computational complexity as well as the need of an accurate system model, which may not be feasible to construct in this era of constantly varying communication standards and user demands. High computational complexity has two particularly adverse consequences in wireless networks: 1) it more rapidly depletes device battery (which runs counter to the entire point of performing efficient power allocation), and 2) the power allocation needs to be recomputed frequently in time-varying channels, undermining the practical utility of a complex algorithm's time-delayed output that lags current channel conditions.
This has led the research community to look for faster, data-driven solutions for power allocation by leveraging machine learning tools.
